Increasing evidence has shown that brain functions are seriously influenced by the heterogeneous structure of a brain network, but little attention has been paid to the aspect of signal propagation. We here study how a signal is propagated from a source node to other nodes on an empirical brain network by a model of bistable oscillators. We find that the unique structure of the brain network favors signal propagation in contrast to other heterogeneous networks and homogeneous random networks. Surprisingly, we find an effect of remote propagation where a signal is not successfully propagated to the neighbors of the source node but to its neighbors' neighbors. To reveal its underlying mechanism, we simplify the heterogeneous brain network into a heterogeneous chain model and find that the accumulation of weak signals from multiple channels makes a strong input signal to the next node, resulting in remote propagation. Furthermore, a theoretical analysis is presented to explain these findings.We investigate the State-Controlled Cellular Neural Network framework of Murali-Lakshmanan-Chua circuit system subjected to two logical signals. By exploiting the attractors generated by this circuit in different regions of phase space, we show that the nonlinear circuit is capable of producing all the logic gates, namely, or, and, nor, nand, Ex-or, and Ex-nor gates, available in digital systems. Further, the circuit system emulates three-input gates and Set-Reset flip-flop logic as well. Moreover, all these logical elements and flip-flop are found to be tolerant to noise. These phenomena are also experimentally demonstrated. Thus, our investigation to realize all logic gates and memory latch in a nonlinear circuit system paves the way to replace or complement the existing technology with a limited number of hardware.The dynamics of four coupled microcells with the oscillatory Belousov-Zhabotinsky (BZ) reaction in them is analyzed with the aid of partial differential equations. Identical BZ microcells are coupled in a circle via identical narrow channels containing all the components of the BZ reaction, which is in the stationary excitable state in the channels. Spikes in the BZ microcells generate unidirectional chemical waves in the channels. A thin filter is put in between the end of the channel and the cell. To make coupling between neighboring cells of the inhibitory type, hydrophobic filters are used, which let only Br2 molecules, the inhibitor of the BZ reaction, go through the filter. To simulate excitatory coupling, we use a hypothetical filter that let only HBrO2 molecules, the activator of the BZ reaction, go through it. New dynamic modes found in the described system are compared with the "old" dynamic modes found earlier in the analogous system of the "single point" BZ oscillators coupled in a circle by pulses with time delay. The "new" and "old" dynamic modes found for inhibitory coupling match well, the only difference being much broader regions of multi-rhythmicity in the "new" dynamic modes. For the excitatory type of coupling, in addition to four symmetrical modes of the "old" type, many new asymmetrical modes coexisting with the symmetrical ones have been found. Asymmetrical modes are characterized by the spikes occurring any time within some finite time intervals.Bone morphogenetic proteins (BMPs) are an important family of growth factors playing a role in a large number of physiological and pathological processes, including bone homeostasis, tissue regeneration, and cancers. In vivo, BMPs bind successively to both BMP receptors (BMPRs) of type I and type II, and a promiscuity has been reported. In this study, we used biolayer interferometry to perform parallel real-time biosensing and to deduce the kinetic parameters (ka, kd) and the equilibrium constant (KD) for a large range of BMP/BMPR combinations in similar experimental conditions. We selected four members of the BMP family (BMP-2, 4, 7, 9) known for their physiological relevance and studied their interactions with five type-I BMP receptors (ALK1, 2, 3, 5, 6) and three type-II BMP receptors (BMPR-II, ACTR-IIA, ACTR-IIB). We reveal that BMP-2 and BMP-4 behave differently, especially regarding their kinetic interactions and affinities with the type-II BMPR. We found that BMP-7 has a higher affinity for the type-II BMPR receptor ACTR-IIA and a tenfold lower affinity with the type-I receptors. While BMP-9 has a high and similar affinity for all type-II receptors, it can interact with ALK5 and ALK2, in addition to ALK1. Interestingly, we also found that all BMPs can interact with ALK5. The interaction between BMPs and both type-I and type-II receptors in a ternary complex did not reveal further cooperativity. Our work provides a synthetic view of the interactions of these BMPs with their receptors and paves the way for future studies on their cell-type and receptor specific signaling pathways.Single-molecule localization microscopy allows practitioners to locate and track labeled molecules in biological systems. When extracting diffusion coefficients from the resulting trajectories, it is common practice to perform a linear fit on mean-squared-displacement curves. However, this strategy is suboptimal and prone to errors. Recently, it was shown that the increments between the observed positions provide a good estimate for the diffusion coefficient, and their statistics are well-suited for likelihood-based analysis methods. Here, we revisit the problem of extracting diffusion coefficients from single-particle tracking experiments subject to static noise and dynamic motion blur using the principle of maximum likelihood. Taking advantage of an efficient real-space formulation, we extend the model to mixtures of subpopulations differing in their diffusion coefficients, which we estimate with the help of the expectation-maximization algorithm. This formulation naturally leads to a probabilistic assignment of trajectories to subpopulations. https://www.selleckchem.com/products/Idarubicin.html We employ the theory to analyze experimental tracking data that cannot be explained with a single diffusion coefficient. We test how well a dataset conforms to the assumptions of a diffusion model and determine the optimal number of subpopulations with the help of a quality factor of known analytical distribution. To facilitate use by practitioners, we provide a fast open-source implementation of the theory for the efficient analysis of multiple trajectories in arbitrary dimensions simultaneously.